Room acoustic parameters such as reverberation time (RT) can be extracted from passively received speech signals by certain ‘blind’ methods, thereby mitigating the need for good controlled excitation signals or prior information of the room geometry. Observation noise which is inevitable in occupied rooms will, however, degrade such methods greatly. In this chapter, a new noise reducing preprocessing which utilizes blind source separation (BSS) and adaptive noise cancellation (ANC) is proposed to reduce the unknown noise from the passively received reverberant speech signal, so that more accurate room acoustic parameters can be extracted. As a demonstration this noise reducing preprocessing is utilized in combination with a maximum-likelihood estimation (MLE)-based method to estimate the RT of a synthetic noise room. Simulation results show that the proposed new approach can improve the accuracy of the RT estimation in a simulated high noise environment. The potential application of the proposed approach for realistic acoustic environments is also discussed, which motivates the need for further development of more sophisticated frequency domain BSS algorithms.
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References
Allen, J.B., Berkley, D.A.: Image method for efficiently simulating small-room acoustics. J. Acoust. Soc. Am. 65, 943–950 (1979)
Araki, S., Mukai, R., Makino, S., Nishikawa, T., Saruwatari, H.: The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech. IEEE Trans. Speech Audio Proces. 11(2), 109–116 (2003)
Cox, T.J., Li, F., Darlington, P.: Extracting room reverberation time from speech using artificial neural networks. J. Audio Eng. Soc. 49, 219–230 (2001)
Greenberg, J.E.: Modified LMS algorithm for speech processing with an adaptive noise canceller. IEEE Trans. Signal Proces. 6(4), 338–351 (1998)
ISO 3382: Acoustics-measurement of the reverberation time of rooms with reference to other acoustical parameters.International Organization for Standardization (1997)
Knaak, M., Araki, S., Makino, S.: Geometrically constrained independent component analysis. IEEE Trans. Speech Audio Proces. 15(2), 715–726 (2007)
Kuttruff, H.: Room Acoustics 4th ed. Spon, London (2000)
Li, F.F.: Extracting room acoustic parameters from received speech signals using artificial neural networks. Ph.D. thesis, Salford University (2002)
Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech.Technical Report BSIS Technical Reports No.98-2, RIKEN Brain Science Institute (1998)
Parra, L., Alvino, C.V.: Geometric source separation: merging convolutive source separation with geometric beamforming.IEEE Trans. Speech Audio Proces. 10(6), 352–362 (2002)
Parra, L., Spence, C.: Convolutive blind source separation of nonstationary sources. IEEE Trans. Speech Audio Proces. 8(3), 320–327 (2000)
Ratnam, R., Jones, D.L., Jr. O’Brien, W.D.: Fast algorithms for blind estimation of reverberation time. IEEE Signal Proces. Lett. 11(6), 537–540 (2004)
Ratnam, R., Jones, D.L., Wheeler, B.C., Jr. O’Brien, W.D., Lansing, C.R., Feng, A.S.: Blind estimation of reverberation time. J. Acoust. Soc. Am. 114(5), 2877–2892 (2003)
Sabine, W.C.: Collected papers on acoustics. Harvard U.P. (1922)
Sawada, H., Mukai, R., Araki, S., Makino, S.: A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Trans. Speech Audio Proces. 12(5), 530–538 (2001)
Schroeder, M.R.: New method for measuring reverberation time. J. Acoust. Soc. Am. 37, 409–412 (1965)
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Zhang, Y., Chambers, J.A. (2008). Acoustic Parameter Extraction From Occupied Rooms Utilizing Blind Source Separation. In: Mandic, D., Golz, M., Kuh, A., Obradovic, D., Tanaka, T. (eds) Signal Processing Techniques for Knowledge Extraction and Information Fusion. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74367-7_4
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